/
optimizer.py
518 lines (451 loc) · 19.2 KB
/
optimizer.py
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__all__ = [
'AutoQuality',
'DEFAULT_DISTRIBUTIONS'
]
import attr
import datetime
import itertools
import logging
import pandas as pd
import random
import time
from copy import deepcopy
from decimal import Decimal
from scipy import stats
from sklearn.model_selection import ParameterSampler
from typing import Optional, Callable, Dict, List, Any
from ..client import Pool, Task, TolokaClient
from ..client.actions import RestrictionV2, SetSkill
from ..client.assignment import GetAssignmentsTsvParameters
from ..client.collectors import AnswerCount, AssignmentSubmitTime, CollectorConfig, MajorityVote, GoldenSet
from ..client.conditions import (
AssignmentsAcceptedCount,
FastSubmittedCount,
TotalSubmittedCount,
TotalAnswersCount,
IncorrectAnswersRate,
GoldenSetIncorrectAnswersRate
)
from ..client.filter import FilterAnd, FilterOr, Skill
from ..client.quality_control import QualityControl
from .scoring import default_calc_scores, default_calc_ranks
logger = logging.getLogger(__name__)
def _create_autoquality_pool_default(autoquality: 'AutoQuality', params: Dict[str, Any], private_name: str):
def _replace_or_create_collector_config(
pool: Pool,
collector: CollectorConfig,
rule: QualityControl.QualityControlConfig.RuleConfig
) -> Pool:
for config in pool.quality_control.configs:
if type(config.collector_config) is type(collector):
config.rules = [rule]
return pool
pool.quality_control.add_action(collector, rule.action, rule.conditions)
return pool
def _configure_quality_control_from_params(pool: Pool) -> Pool:
if 'overlap' in params:
overlap = params['overlap']
pool.defaults = Pool.Defaults(
default_overlap_for_new_task_suites=overlap,
)
else:
overlap = pool.defaults.default_overlap_for_new_task_suites
if 'AssignmentSubmitTime' in params:
history_size = int(params['AssignmentSubmitTime']['history_size'])
avg_page_seconds = int(params['AssignmentSubmitTime']['avg_page_seconds'])
too_fast_fraction = float(params['AssignmentSubmitTime']['too_fast_fraction'])
fast_submit_threshold_seconds = int(avg_page_seconds * too_fast_fraction)
fast_submitted_count = history_size
fast_submitted_count = min(
fast_submitted_count,
history_size,
)
pool = _replace_or_create_collector_config(
pool,
AssignmentSubmitTime(
history_size=history_size,
fast_submit_threshold_seconds=fast_submit_threshold_seconds
),
QualityControl.QualityControlConfig.RuleConfig(
conditions=[
TotalSubmittedCount >= history_size,
FastSubmittedCount >= fast_submitted_count
],
action=RestrictionV2(
scope='POOL',
duration=1,
duration_unit='DAYS',
private_comment='AssignmentSubmitTime',
),
)
)
if 'MajorityVote' in params:
history_size = params['MajorityVote']['history_size']
answer_threshold = overlap - 1
incorrect_answers_rate = params['MajorityVote']['incorrect_answers_rate']
pool = _replace_or_create_collector_config(
pool,
MajorityVote(
answer_threshold=answer_threshold,
history_size=history_size,
),
QualityControl.QualityControlConfig.RuleConfig(
conditions=[
TotalAnswersCount >= history_size,
IncorrectAnswersRate > incorrect_answers_rate,
],
action=RestrictionV2(
scope='POOL',
duration=1,
duration_unit='DAYS',
private_comment='MajorityVote',
),
)
)
if 'GoldenSet' in params:
history_size = params['GoldenSet']['history_size']
incorrect_answers_rate = params['GoldenSet']['incorrect_answers_rate']
pool = _replace_or_create_collector_config(
pool,
GoldenSet(history_size=history_size),
QualityControl.QualityControlConfig.RuleConfig(
conditions=[GoldenSetIncorrectAnswersRate >= incorrect_answers_rate],
action=RestrictionV2(
scope='POOL',
duration=1,
duration_unit='DAYS',
private_comment='GoldenSet',
)
)
)
if 'TrainingRequirement' in params:
training_passing_skill_value = params['TrainingRequirement']['training_passing_skill_value']
pool.quality_control.training_requirement = QualityControl.TrainingRequirement(
training_pool_id=autoquality.training_pool_id,
training_passing_skill_value=training_passing_skill_value,
)
if 'ExamRequirement' in params and autoquality.exam_skill_id:
pool = _configure_exam(
pool,
autoquality.exam_skill_id,
params['ExamRequirement']['exam_passing_skill_value']
)
return pool
def _configure_exam(pool: Pool, exam_skill_id, exam_passing_skill_value) -> Pool:
exam_filter = (Skill(exam_skill_id) >= exam_passing_skill_value)
if pool.filter is None:
pool.filter = exam_filter
return pool
if not isinstance(pool.filter, FilterAnd):
pool.filter = FilterAnd([pool.filter])
has_filter = False
for filter in pool.filter:
if isinstance(filter, (FilterOr, FilterAnd)):
sub_filters = list(filter)
filter = sub_filters[0] if len(sub_filters) == 1 else filter
if isinstance(filter, Skill) and filter.key == exam_skill_id:
filter.value = exam_passing_skill_value
has_filter = True
if not has_filter:
pool.filter &= exam_filter
return pool
pool = autoquality.toloka_client.clone_pool(autoquality.base_pool_id)
pool = _configure_quality_control_from_params(pool)
pool.private_name = private_name
pool = autoquality.toloka_client.update_pool(pool.id, pool)
skill_name = pool.private_name
autoquality.autoquality_pool_skills[pool.id] = autoquality._create_skill_if_not_exists(skill_name)
return pool
DEFAULT_DISTRIBUTIONS = dict(
overlap=stats.planck(0.8, loc=2),
AssignmentSubmitTime=dict(
history_size=[5],
avg_page_seconds=[90],
too_fast_fraction=stats.skewnorm(a=2, loc=0.2, scale=0.15),
),
MajorityVote=dict(
history_size=[5],
incorrect_answers_rate=stats.norm(loc=60, scale=15),
),
GoldenSet=dict(
history_size=[5],
incorrect_answers_rate=stats.norm(loc=60, scale=15),
),
TrainingRequirement=dict(
training_passing_skill_value=stats.norm(loc=50, scale=20),
),
ExamRequirement=dict(
exam_passing_skill_value=stats.norm(loc=50, scale=20),
),
)
def _get_default_distribs():
return DEFAULT_DISTRIBUTIONS
@attr.s(auto_attribs=True)
class AutoQuality:
"""This class implements a tool to help set up quality control for Toloka project.
To use `toloka.autoquality` install toloka-kit via `pip install toloka-kit[autoquality]`
Attributes:
toloka_client: TolokaClient instance to interact with requester's account
project_id: Toloka project ID
base_pool_id: Template Pool for autoquality pools
training_pool_id: Training Pool ID
exam_pool_id: Exam Pool ID
exam_skill_id: Skill for filtering by exam perfomance
label_field: Output field name
n_iter: Number of an autoquality pools
parameter_distributions: Parameter distributions
score_func: Callable to calculate pool scores
ranking_func: Callabale to ranking pools based on their scores
create_autoquality_pool_func: Callable to create autoquality pool
run_id: ID of autoquality run
Example:
>>> aq = AutoQuality(
>>> toloka_client=toloka_client,
>>> project_id=...,
>>> base_pool_id=...,
>>> training_pool_id=...,
>>> exam_pool_id = ...,
>>> exam_skill_id = ...
>>> )
>>> aq.setup_pools()
>>> aq.create_tasks(aq_tasks)
>>> aq.run()
>>> aq.best_pool_params
...
"""
toloka_client: TolokaClient
project_id: str
base_pool_id: str
training_pool_id: str
exam_pool_id: Optional[str] = None
exam_skill_id: Optional[str] = None
label_field: str = 'label'
n_iter: int = 10
parameter_distributions: Dict = attr.ib(factory=_get_default_distribs)
score_func: Callable = default_calc_scores
ranking_func: Callable = default_calc_ranks
create_autoquality_pool_func: Callable = _create_autoquality_pool_default
run_id: str = attr.attrib(
init=False,
default=f'AutoQuality Project {datetime.datetime.strftime(datetime.datetime.now(), "%Y-%m-%d %H:%M:%S")}'
)
# internal fields
autoquality_pools: List[Pool] = attr.attrib(init=False, factory=list)
autoquality_pool_skills: Dict[str, Skill] = attr.attrib(init=False, factory=dict)
worker_autoquality_pool_skills: Dict[str, str] = attr.attrib(init=False, factory=dict)
params: Dict[str, Dict[str, Any]] = attr.attrib(init=False, factory=dict)
_base_pool: Optional[Pool] = attr.attrib(init=False, default=None)
_assigned_workers: Dict[str, str] = attr.attrib(init=False, factory=dict)
_scores: Optional[Dict[str, Any]] = attr.attrib(init=False, default=None)
_ranks: Optional[pd.DataFrame] = attr.attrib(init=False, default=None)
_pruned_params: Optional[Dict[str, Dict[str, Any]]] = attr.attrib(init=False, default=None)
def setup_pools(self):
"""Create autoquality pools with sampled quality control parameters.
"""
logger.info('Creating pools')
for params in ParameterSampler(
self._params_to_flat_dict(self.parameter_distributions),
self.n_iter * 10,
):
if len(self.params) >= self.n_iter:
break
try:
params = self._params_from_flat_dict(params)
pool = self.create_autoquality_pool_func(
self,
params,
private_name=f'{self.run_id} params {len(self.autoquality_pools)}'
)
self.autoquality_pools.append(pool)
self.params[pool.id] = params
logger.info(params)
except Exception as e:
logger.error(f'Exception when initializing pool, params discarded: {e}')
self._setup_pool_skill_filters()
def create_tasks(self, tasks: List[Task]):
"""Add tasks to autoquality pools.
If the GoldenSet rule is used in quality control then control tasks should also be provided.
"""
logger.info('Creating tasks in pools')
for pool in self.autoquality_pools:
self._create_tasks(pool.id, tasks)
logger.info('Setup complete, please verify')
return
def run(self):
"""Run autoquality process.
"""
try:
logger.info('Opening pools')
self._open_pools()
logger.info('Waiting for all pools to close')
self._wait_pool_for_all_pools_to_close()
except Exception as e:
raise e
finally:
self._close_pools()
@property
def base_pool(self):
if not self._base_pool:
self._base_pool = self.toloka_client.get_pool(self.base_pool_id)
return self._base_pool
@property
def scores(self):
if self._scores is None:
self._scores = self._calc_scores()
return self._scores
@property
def ranks(self):
if self._ranks is None:
self._ranks = self._calc_ranks()
return self._ranks
@property
def pruned_params(self):
if self._pruned_params is None:
self._pruned_params = self._calc_pruned_params()
return self._pruned_params
@property
def best_pool_id(self):
return self.ranks[self.ranks.main_rank == self.ranks.main_rank.max()].pool_id.item()
@property
def best_pool(self):
for pool in self.autoquality_pools:
if pool.id == self.best_pool_id:
return pool
@property
def best_pool_params(self):
return self.params[self.best_pool_id]
@property
def best_pruned_params(self):
return self.pruned_params[self.best_pool_id]
@staticmethod
def _params_to_flat_dict(params):
new_dict = {}
for k, v in params.items():
if isinstance(v, dict):
nested_dict = AutoQuality._params_to_flat_dict(v)
for param, val in nested_dict.items():
new_dict[f'{k}__{param}'] = val
else:
new_dict[k] = v
return new_dict
@staticmethod
def _params_from_flat_dict(params):
new_dict = {}
for k, v in params.items():
parts = k.split('__')
if len(parts) > 1:
nested_dict_key = parts[0]
param_key = parts[1]
if not new_dict.get(nested_dict_key):
new_dict[nested_dict_key] = {}
new_dict[nested_dict_key][param_key] = v
else:
new_dict[k] = v
return new_dict
def _create_skill_if_not_exists(self, skill_name):
skill = next(self.toloka_client.get_skills(name=skill_name), None)
if skill:
return skill
return self.toloka_client.create_skill(
name=skill_name,
hidden=True,
)
def _create_tasks(self, pool_id: str, tasks_data: List[Task]):
tasks = []
for task in deepcopy(tasks_data):
task.pool_id = pool_id
tasks.append(task)
self.toloka_client.create_tasks(tasks=tasks, allow_defaults=True)
logger.info(f'Populated pool {pool_id} with {len(tasks)} tasks')
def _setup_pool_skill_filters(self):
for pool in self.autoquality_pools:
pool_skill = self.autoquality_pool_skills[pool.id]
pool.quality_control.add_action(
collector=AnswerCount(),
conditions=[AssignmentsAcceptedCount > 0],
action=SetSkill(skill_id=pool_skill.id, skill_value=1),
)
for other_pool in self.autoquality_pools:
if other_pool.id != pool.id:
pool.filter &= (Skill(self.autoquality_pool_skills[other_pool.id]) != 1)
self.toloka_client.update_pool(pool.id, pool)
def _open_pools(self):
self.toloka_client.open_pool(self.training_pool_id)
if self.exam_pool_id:
self.toloka_client.open_pool(self.exam_pool_id)
for i in range(len(self.autoquality_pools)):
pool = self.toloka_client.get_pool(self.autoquality_pools[i].id)
if pool.is_closed() and pool.last_close_reason == Pool.CloseReason.COMPLETED:
continue
self.autoquality_pools[i] = self.toloka_client.open_pool(self.autoquality_pools[i].id)
def archive_autoquality_pools(self):
"""Archive all pools created by `AutoQuality.setup_pools`
"""
for i in range(len(self.autoquality_pools)):
self.autoquality_pools[i] = self.toloka_client.archive_pool(self.autoquality_pools[i].id)
def _close_pools(self):
self.toloka_client.close_pool(self.training_pool_id)
if self.exam_pool_id:
self.toloka_client.close_pool(self.exam_pool_id)
for i in range(len(self.autoquality_pools)):
self.autoquality_pools[i] = self.toloka_client.close_pool(self.autoquality_pools[i].id)
def _assign_pool_skills(self, from_pool_id, pool_skills):
pool_skills_copy = list(pool_skills)
random.shuffle(pool_skills_copy)
pool_skills_cycle = itertools.cycle(pool_skills_copy)
df = self.toloka_client.get_assignments_df(from_pool_id,
field=[GetAssignmentsTsvParameters.Field.WORKER_ID],
exclude_banned=True)
workers = df['ASSIGNMENT:worker_id'].unique()
for worker_id in workers:
if worker_id not in self.worker_autoquality_pool_skills:
skill = next(pool_skills_cycle)
self.toloka_client.set_user_skill(
skill_id=skill.id,
user_id=worker_id,
value=Decimal(1),
)
self.worker_autoquality_pool_skills[worker_id] = skill.id
def _wait_pool_for_all_pools_to_close(self, minutes_to_wait=0.3):
sleep_time = 60 * minutes_to_wait
open_pools = self.autoquality_pools
while open_pools:
open_pools = []
for pool in self.autoquality_pools:
pool = self.toloka_client.get_pool(pool.id)
if pool.is_open():
open_pools.append(pool)
if open_pools:
training_pool = self.toloka_client.get_pool(self.training_pool_id)
if training_pool.is_open():
pool_skills = [self.autoquality_pool_skills[pool.id] for pool in open_pools]
self._assign_pool_skills(training_pool.id, pool_skills)
time.sleep(sleep_time)
def _calc_scores(self):
scores = dict()
for pool in self.autoquality_pools:
score = None
try:
score = self.score_func(self.toloka_client, pool.id, self.label_field)
except Exception as e:
logger.error(f'Exception when computing pool scores, pool skipped: {e}')
scores[pool.id] = dict(
params=self.params[pool.id],
score=score,
)
return scores
def _calc_ranks(self):
scores_df = pd.DataFrame([dict(pool_id=pool, **s['score'], **s['params']) for pool, s in self.scores.items() if
s['score'] is not None])
return self.ranking_func(scores_df)
def _calc_pruned_params(self):
pruned_params = dict()
keep_params = ['ExamRequirement', 'TrainingRequirement', 'overlap']
for pool_id, pool_score in self.scores.items():
if not pool_score['score']:
continue
pruned_params[pool_id] = {param_name: param_config for param_name, param_config in
pool_score['params'].items()
if pool_score['score']['ban_reason_counts'].get(
param_name) or param_name in keep_params}
return pruned_params